Patents by Inventor Si-Zhao Qin

Si-Zhao Qin has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10955818
    Abstract: A method for extracting a set of principal time series data of dynamic latent variables. The method includes detecting, by a plurality of sensors, dynamic samples of data each corresponding to one of a plurality of original variables. The method also includes analyzing, using a controller, the dynamic samples of data to determine a plurality of latent variables that represent variation in the dynamic samples of data. The method also includes selecting, by the controller, at least one inner latent variable that corresponds to at least one of the plurality of original variables. The method also includes estimating an estimated current value of the at least one inner latent variable based on previous values of the at least one inner latent variable.
    Type: Grant
    Filed: March 20, 2018
    Date of Patent: March 23, 2021
    Assignee: University of Southern California
    Inventors: Si-Zhao Qin, Yining Dong
  • Publication number: 20200348659
    Abstract: Methods and systems for building and maintaining model(s) of a physical process are disclosed. One method includes receiving training data associated with a plurality of different data sources, and performing a clustering process to form one or more clusters. For each of the one or more clusters, the method includes building a data model based on the training data associated with the data sources in the cluster, automatically performing a data cleansing process on operational data based on the data model, and automatically updating the data model based on updated training data that is received as operational data. For data sources excluded from the clusters, automatic building, data cleansing, and updating of models can also be applied.
    Type: Application
    Filed: May 1, 2020
    Publication date: November 5, 2020
    Applicants: Chevron U.S.A. Inc., University of Southern California
    Inventors: Yining DONG, Alisha DESHPANDE, Yingying ZHENG, Lisa Ann BRENSKELLE, Si-Zhao QIN
  • Publication number: 20180267503
    Abstract: A method for extracting a set of principal time series data of dynamic latent variables. The method includes detecting, by a plurality of sensors, dynamic samples of data each corresponding to one of a plurality of original variables. The method also includes analyzing, using a controller, the dynamic samples of data to determine a plurality of latent variables that represent variation in the dynamic samples of data. The method also includes selecting, by the controller, at least one inner latent variable that corresponds to at least one of the plurality of original variables.
    Type: Application
    Filed: March 20, 2018
    Publication date: September 20, 2018
    Inventors: Si-Zhao Qin, Yining Dong
  • Patent number: 9964967
    Abstract: A method for monitoring a control of a parameter of one or more devices or systems in an oil or gas production site includes receiving process data, the process data being a result of the control of the parameter of the one or more devices or systems in the production site; smoothing the process data using a polynomial filter while preserving features of the process data to obtain smoothed data; and applying a pattern recognition algorithm to the smoothed data to determine whether there is a malfunction condition in the one or more devices or systems.
    Type: Grant
    Filed: February 22, 2013
    Date of Patent: May 8, 2018
    Assignees: CHEVRON U.S.A. INC., UNIVERSITY OF SOUTHERN CALIFORNIA
    Inventors: Yingying Zheng, Si-Zhao Qin, Michael Barham
  • Publication number: 20160179599
    Abstract: A computer-implemented method for reconstructing data includes receiving a selection of one or more input data streams at a data processing framework. The method can include determining existence of a fault in the input data stream(s). This determination can be based on receiving a definition of one or more analytics components at the data processing framework and applying a dynamic principal component analysis (DPCA) to the input data streams. Detection of the fault can be based at least in part on a prediction error and a variation in principal component subspace generated based on the DPCA. Detection of the fault can also be based on performing a wavelet transform to generate a set of coefficients defining the data stream, the set of coefficients including one or more coefficients representing a high frequency portion of data included in the data stream. The method can include reconstructing data at the fault.
    Type: Application
    Filed: November 10, 2015
    Publication date: June 23, 2016
    Applicants: University of Southern California, Chevron U.S.A. Inc.
    Inventors: Alisha Deshpande, Yining Dong, Gang Li, Yingying Zheng, Si-Zhao Qin, Lisa Ann Brenskelle
  • Publication number: 20140108359
    Abstract: Methods and systems for reconstructing data are disclosed. One method includes receiving a selection of one or more input data streams at a data processing framework, and receiving a definition of one or more analytics components at the data processing framework. The method further includes applying a dynamic principal component analysis to the one or more input data streams, and detecting a fault in the one or more input data streams based at least in part on a prediction error and a variation in principal component subspace generated based on the dynamic principal component analysis. The method also includes reconstructing data at the fault within the one or more input data streams based on data collected prior to occurrence of the fault.
    Type: Application
    Filed: February 28, 2013
    Publication date: April 17, 2014
    Inventors: Farnoush Banaei-Kashani, Yingying Zheng, Si-Zhao Qin, Mohammad Asghari, Mahdi Rahmani Mofrad, Cyrus Shahabi, Lisa A. Brenskelle